Electronic surveillance of areas and locations to detect changes in the background, either by the addition of new objects into a particular scene or the removal of stationary objects, has various applications. Some examples include:    (a) detecting suspicious packages or abandoned luggage in airports or other transportation terminals,    (b) monitoring valuables in museums, art galleries or other secure areas,    (c) monitoring traffic flow at busy intersections, or detecting blocked road tunnels and bridges, and    (d) monitoring parking areas.
Current methods of surveillance include a human operator monitoring various areas by the use of closed circuit television. This invariably leads to human operator error or fatigue, especially so when the operator is confronted with a dynamic scene such as a busy airport terminal. These scenes typically contain a number of moving people (or vehicles) that regularly occlude the background. As a result, stationary objects in a particular scene can be obscured for some significant periods of time by moving objects.
Furthermore, a transient object in a particular scene may often pause for short periods of time, such as a person stopping to purchase or enjoy a drink or a car waiting before entering traffic flow. Such temporary pauses are of course, not genuine changes to the background.
Automatic image detection systems ideally operate in real-time, produce few false alarms, and are capable of operating using existing hardware, typically current cameras and computing platforms.
In view of the above observations, a need clearly exists for an image analysis techniques that at least addresses deficiencies of visual motion surveillance systems.